Imperial College London
Alternate

ProfessorMarkJohnson

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Clinical Chair in Obstetrics
 
 
 
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Contact

 

+44 (0)20 3315 7887mark.johnson

 
 
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Location

 

H3.35Chelsea and Westminster HospitalChelsea and Westminster Campus

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Summary

 

Publications

Citation

BibTex format

@article{Stanfield:2019:10.3389/fgene.2019.00185,
author = {Stanfield, Z and Johnson, MR and Blanks, AM and Romero, R and Chance, MR and Mesiano, S and Koyuturkm, M},
doi = {10.3389/fgene.2019.00185},
journal = {Frontiers in Genetics},
title = {Myometrial transcriptional signatures of human parturition},
url = {http://dx.doi.org/10.3389/fgene.2019.00185},
volume = {10},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The process of parturition involves the transformation of the quiescent myometrium (uterine smooth muscle) to the highly contractile laboring state. This is thought to be driven by changes in gene expression in myometrial cells. Despite the existence of multiple myometrial gene expression studies, the transcriptional programs that initiate labor are not known. Here, we integrated three transcriptome datasets, one novel (NCBI Gene Expression Ominibus: GSE80172) and two existing, to characterize the gene expression changes in myometrium associated with the onset of labor at term. Computational analyses including classification, singular value decomposition, pathway enrichment, and network inference were applied to individual and combined datasets. Outcomes across studies were integrated with multiple protein and pathway databases to build a myometrial parturition signaling network. A high-confidence (significant across all studies) set of 126 labor genes were identified and machine learning models exhibited high reproducibility between studies. Labor signatures included both known (interleukins, cytokines) and unknown (apoptosis, MYC, cell proliferation/differentiation) pathways while cyclic AMP signaling and muscle relaxation were associated with non-labor. These signatures accurately classified and characterized the stages of labor. The data-derived parturition signaling networks provide new genes/signaling interactions to understand phenotype-specific processes and aid in future studies of parturition.
AU  - Stanfield,Z
AU  - Johnson,MR
AU  - Blanks,AM
AU  - Romero,R
AU  - Chance,MR
AU  - Mesiano,S
AU  - Koyuturkm,M
DO  - 10.3389/fgene.2019.00185
PY  - 2019///
SN  - 1664-8021
TI  - Myometrial transcriptional signatures of human parturition
T2  - Frontiers in Genetics
UR  - http://dx.doi.org/10.3389/fgene.2019.00185
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000463108600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/71384
VL  - 10
ER  -
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